ABSTRACT
Aims: there is increasing awareness that for effective patient care we need more than only randomized controlled trials with groups of participants and that carefully collected single case (N = 1) data have several important advantages over traditional group-level studies. With the advance of technology, collecting relevant data from a single case is becoming easier by the day, and this offers tremendous opportunities for understanding how behaviors displayed by an individual can be influenced by one or several key variables. For example, how pain experienced influences the amount of time spent on physical exercise. Method: using publicly available observational single case data, five models are compared: a classical ordinary least squares (OLS) linear regression model; a dynamic regression model (DRM); a two-level random-intercepts model (2LRI); a continuous covariate first-order autoregressive correlation model (CAR1); and an ordinary least squares model with time trend (OLST). These models are compared in terms of overall model fit statistics, estimates of the relation between physical activity (response variable of interest) and pain (covariate of interest), and residual statistics. Results: 2LRI outperforms all other models on both overall model fit and residual statistics, and provides covariate estimates that are in between the relative extremes provided by other models. CAR1 and OLST demonstrate an almost identical performance and one that is substantially better than OLS which performs worst and DRM. Conclusion: for observational single case data, DRM, CAR1, OLST, and 2LRI account for the serial correlation that is typically present in single case data in somewhat different ways under somewhat different assumptions, and all perform better than OLS. Implications of these findings for observational, quasi-experimental, and experimental single case studies are discussed.
Objetivos: há uma crescente conscientização de que, para um atendimento eficaz ao paciente, precisamos de mais do que apenas ensaios clínicos randomizados com grupos de participantes e que os dados de caso único cuidadosamente coletados (N = 1) têm várias vantagens importantes sobre os estudos tradicionais em nível de grupo. Com o avanço da tecnologia, coletar dados relevantes de um único caso está se tornando mais fácil a cada dia, e isso oferece enormes oportunidades para entender como os comportamentos exibidos por um indivíduo podem ser influenciados por uma ou várias variáveis-chave. Por exemplo, como a dor experimentada influencia a quantidade de tempo gasto no exercício físico. Método: usando dados de caso único observacionais disponíveis publicamente, cinco modelos são comparados: um modelo clássico de regressão linear de mínimos quadrados ordinários (OLS); um modelo de regressão dinâmica (DRM); um modelo de interceptações aleatórias de dois níveis (2LRI); um modelo de correlação autorregressiva de primeira ordem covariável contínua (CAR1); e um modelo ordinário de mínimos quadrados com tendência temporal (OLST). Esses modelos são comparados em termos de estatísticas gerais de ajuste do modelo, estimativas da relação entre atividade física (variável de resposta de interesse) e dor (covariável de interesse) e estatísticas residuais. Resultados: o 2LRI supera todos os outros modelos tanto no ajuste geral do modelo quanto nas estatísticas residuais e fornece estimativas de covariáveis que estão entre os extremos relativos fornecidos por outros modelos. CAR1 e OLST demonstram um desempenho quase idêntico e substancialmente melhor que o OLS, que apresenta o pior desempenho, e o DRM. Conclusão: para dados observacionais de caso único, DRM, CAR1, OLST e 2LRI são responsáveis pela correlação seriada que normalmente está presente em dados de caso único de maneira um pouco diferentes sob suposições um pouco diversas, e todos têm um desempenho melhor que o OLS. Implicações dessas descobertas para estudos de caso único observacionais, quase-experimentais e experimentais são discutidas.
Subject(s)
Humans , Male , Adult , Pain , Exercise , Methods , Technology , Least-Squares Analysis , Linear Models , Patient CareABSTRACT
In order to realize the rapid and non-destructive detection of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix, this paper first prepared the sulphur-fumigated Achyranthis Bidentatae Radix samples with the usage amount of sulphur being 0, 2.5%, and 5% of the mass of Achyranthis Bidentatae Radix pieces. The SO_2 content in different batches of sulphur-fumigated Achyranthis Bidentatae Radix was determined using the method in Chinese Pharmacopoeia, followed by the acquisition of their hyperspectral data within both visible-near infrared(435-1 042 nm) and short-wave infrared(898-1 751 nm) regions by hyperspectral imaging. Meanwhile, the first derivative, AUTO, multiplicative scatter correction, Savitzky-Golay(SG) smoothing, and standard normal variable transformation algorithms were used to pre-process the original hyperspectral data, which were then subjected to characteristic band extraction based on competitive adaptive reweighted sampling(CARS) and the partial least square regression analysis for building a quantitative model of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix. It was found that the accuracy of the quantitative model built depending on the visible-near infrared spectra was high, with the determination coefficient of prediction set(R■) reaching 0.900 1. The established quantitative model has enabled the rapid and non-destructive detection of SO_2 content in sulphur-fumigated Achyranthis Bidentatae Radix, which can serve as an effective supplement to the method described in Chinese Pharmacopeia.
Subject(s)
Hyperspectral Imaging , Least-Squares Analysis , Plant Roots , SulfurABSTRACT
This study established a method for rapid quantification of terpene lactone, bilobalide, ginkgolide C, ginkgolide A and ginkgolide B in the chromatographic process of Ginkgo Folium based on near infrared spectroscopy(NIRS). The effects of competitive adaptive reweighting sampling(CARS), random frog(RF), and synergy interval partial least squares(siPLS) on the performance of partial least squares regression(PLSR) model were compared to the reference values measured by HPLC. Among them, the correlation coefficients of prediction(Rp) of validation sets of terpene lactone, bilobalide, and ginkgolide C were all higher than 0.98, and the relative standard errors of prediction(RSEPs) were 5.87%, 6.90% and 6.63%, respectively. Aiming at ginkgolide A and ginkgolide B with relatively low content, the genetic algorithm joint extreme learning machine(GA-ELM) was used to establish the optimized quantitative analysis model. Compared with CARS-PLSR model, the CARS-GA-ELM models of ginkgolide A and ginkgolide B exhibited a reduction in RSEP from 15.65% to 8.52% and from 21.28% to 10.84%, respectively, which met the needs of quantitative ana-lysis. It has been proved that NIRS can be used for the rapid detection of various lactone components in the chromatographic process of Ginkgo Folium.
Subject(s)
Chromatography, High Pressure Liquid , Ginkgo biloba , Lactones/analysis , Least-Squares Analysis , Spectroscopy, Near-Infrared/methodsABSTRACT
OBJECTIVE@#To investigate the performance of different low-dose CT image reconstruction algorithms for detecting intracerebral hemorrhage.@*METHODS@#Low-dose CT imaging simulation was performed on CT images of intracerebral hemorrhage at 30%, 25% and 20% of normal dose level (defined as 100% dose). Seven algorithms were tested to reconstruct low-dose CT images for noise suppression, including filtered back projection algorithm (FBP), penalized weighted least squares-total variation (PWLS-TV), non-local mean filter (NLM), block matching 3D (BM3D), residual encoding-decoding convolutional neural network (REDCNN), the FBP convolutional neural network (FBPConvNet) and image restoration iterative residual convolutional network (IRLNet). A deep learning-based model (CNN-LSTM) was used to detect intracerebral hemorrhage on normal dose CT images and low-dose CT images reconstructed using the 7 algorithms. The performance of different reconstruction algorithms for detecting intracerebral hemorrhage was evaluated by comparing the results between normal dose CT images and low-dose CT images.@*RESULTS@#At different dose levels, the low-dose CT images reconstructed by FBP had accuracies of detecting intracerebral hemorrhage of 82.21%, 74.61% and 65.55% at 30%, 25% and 20% dose levels, respectively. At the same dose level (30% dose), the images reconstructed by FBP, PWLS-TV, NLM, BM3D, REDCNN, FBPConvNet and IRLNet algorithms had accuracies for detecting intracerebral hemorrhage of 82.21%, 86.80%, 89.37%, 81.43%, 90.05%, 90.72% and 93.51%, respectively. The images reconstructed by IRLNet at 30%, 25% and 20% dose levels had accuracies for detecting intracerebral hemorrhage of 93.51%, 93.51% and 93.06%, respectively.@*CONCLUSION@#The performance of reconstructed low-dose CT images for detecting intracerebral hemorrhage is significantly affected by both dose and reconstruction algorithms. In clinical practice, choosing appropriate dose level and reconstruction algorithm can greatly reduce the radiation dose and ensure the detection performance of CT imaging for intracerebral hemorrhage.
Subject(s)
Algorithms , Cerebral Hemorrhage/diagnostic imaging , Humans , Image Processing, Computer-Assisted/methods , Least-Squares Analysis , Tomography, X-Ray Computed/methodsABSTRACT
Introducción: Las motivaciones para elegir las carreras universitarias determinan en buena medida el desempeño profesional, de allí la necesidad de contar con instrumentos válidos y confiables para su estudio. Objetivo: Validar una escala para evaluar las motivaciones para estudiar Estomatología en alumnos cubanos. Métodos: Estudio de tipo instrumental, transversal y multicéntrico, que incluyó estudiantes de nueve universidades cubanas. A partir de un instrumento en español validado en estudiantes latinoamericanos de medicina, se realizó un análisis factorial exploratorio por mínimos cuadrados no ponderados. Luego se realizó un análisis factorial confirmatorio y se midió la consistencia interna con el alpha de Cronbach. Resultados: Se incluyó a 1324 participantes, de los cuales el 66,8 por ciento fueron mujeres y la media de la edad fue 21,2 ± 1,8 años. Sobre la base de una matriz de correlaciones, la prueba de Bartlett arrojó indicadores significativos (p < 0,05) y el índice KMO fue superior a 0,8. La varianza explicada fue superior al 50 por ciento y el análisis paralelo sugirió solo 2 factores. De la escala inicial, el análisis factorial sugirió eliminar los ítems 4 y 5 (factor 1), 9 y 12 (factor 2) y el ítem 1, por lo que el modelo quedó conformado por 7 ítems, 3 para el factor 1 y 4 para el factor 2. El ajuste e índices fueron adecuados, lo que demostró validez de constructo. Conclusión: La escala de motivaciones para estudiar Estomatología demostró ser válida y confiable, y está conformada por dos dominios que denotan aspectos sociales y económicos(AU)
Introduction: The motivations for the choice of university studies determine professional performance to a considerable extent. Hence the need for valid, reliable tools to evaluate them. Objective: Validate a scale to evaluate motivational reasons to study dentistry among Cuban students. Methods: An instrumental cross-sectional multicenter study was conducted which included students from nine Cuban universities. Based on a tool in Spanish validated in Latin American medical students, exploratory factor analysis was performed by unweighted least squares. Confirmatory factor analysis was then carried out, and internal consistency was measured with Cronbach's alpha. Results: A total 1 324 participants were included, of whom 66.8 percent were women; mean age was 21.2 ± 1.8 years. Based on a correlation matrix, Bartlett's test yielded significant indicators (p < 0.05), and the KMO index was above 0.8. Explained variance was above 50 percent, and parallel analysis suggested only two factors. Factor analysis suggested to remove the following items from the initial scale: 4 and 5 (factor 1), 9 and 12 (factor 2) and 1, as a result of which the model would consist of 7 items: 3 for factor 1 and 4 for factor 2. The adjustment and the indices were appropriate, which showed construct validity. Conclusion: The scale for motivations to study dentistry was found to be valid and reliable. It consists of two domains denoting social and economic aspects(AU)
Subject(s)
Humans , Male , Female , Students, Medical , Universities , Least-Squares Analysis , Demography , Oral Medicine , Dentistry , Motivation , Cross-Sectional Studies , Multicenter StudyABSTRACT
Abstract The purpose of this research was to discriminate soil fractions using mineralogical and elemental analyses and to show those fractions that present greater contribution to the soil mass attenuation coefficient (μ) as well as their partial cross-sections for photoelectric absorption (PA), coherent scattering (CS) and incoherent scattering (IS). Soil samples from different places of Brazil classified as Yellow Argisol, Yellow Latosol and Gray Argisol were submitted to elemental and mineralogical analyses through energy dispersive X-ray fluorescence (EDXRF) and Rietveld Method with X-ray diffraction data (RM-XRD). The mixture rule was utilized to calculate μ of each soil. The EDXRF analysis showed as predominant elements Si, Al, Fe and Ti oxides. The highest contents were Si (914.3 to 981.3 g kg-1) in the sand fractions, Al (507.9 to 543.7 g kg-1) and Fe (32.5 to 76.7 g kg-1) in the clay fractions, and Ti (18.0 to 59.0 g kg-1) in the silt fractions. The RM-XRD allowed identifying that the sand fractions are predominantly made of quartz (913.3 to 995.0 g kg-1), while the clay greatest portion is made of kaolinite (465.0 to 660.6 g kg-1) and halloysite (169.0 to 385.0 g kg-1). The main effect responsible for μ was IS (50 to 61.4%) followed by PA (28 to 40.1%) and CS (9.9 to 10.6%). By using the principal component analysis (PC-1: 57.5% and PC-2: 20.9%), the samples were differentiated through the discrimination between physical, chemical and mineralogical properties. The results obtained suggest that general information about the radiation interaction in soils can be obtained through the elemental and mineralogical analyses of their fractions.
Subject(s)
Soil Characteristics/analysis , Disaster Management , Least-Squares Analysis , Principal Component AnalysisABSTRACT
In order to establish a rapid and non-destructive evaluation method for the identification of Armeniacae Semen Amarum and Persicae Semen from different origins, the spectral information of Armeniacae Semen Amarum and Persicae Semen in the range of 898-1 751 nm was collected based on hyperspectral imaging technology. Armeniacae Semen Amarum and Persicae Semen from different origins were collected as research objects, and a total of 720 Armeniacae Semen Amarum samples and 600 Persicae Semen samples were used for authenticity discrimination. The region of interest(ROI) and the average reflection spectrum in the ROI were obtained, followed by comparing five pre-processing methods. Then, partial least squares discriminant analysis(PLS-DA), support vector machine(SVM), and random forest(RF) method were established for classification models, which were evaluated by the confusion matrix of prediction results and receiver operating characteristic curve(ROC). The results showed that in the three sample sets, the se-cond derivative pre-processing method and PLS-DA were the best model combinations. The classification accuracy of the test set under the 5-fold cross-va-lidation was 93.27%, 96.19%, and 100.0%, respectively. It was consistent with the confusion matrix of the predicted results. The area under the ROC curve obtained the highest values of 0.992 3, 0.999 6, and 1.000, respectively. The study revealed that the near-infrared hyperspectral imaging technology could accurately identify the medicinal materials of Armeniacae Semen Amarum and Persicae Semen from different origins and distinguish the authentication of these two varieties.
Subject(s)
Drugs, Chinese Herbal , Hyperspectral Imaging , Least-Squares Analysis , Semen , Support Vector Machine , TechnologyABSTRACT
Three cancer cell lines including gastric cancer SGC-7901, HGC-27, and MGC-803 cells were employed to evaluate the bioactivity of seven Dendrobium species. Simultaneously, these Dendrobium species were assessed with UPLC-Q-TOF-MS, and 504 common peaks were found. Based on the hypothesis that biological effects varied with differences in components, multivariate relevance analysis for chemical component-activity relationship of Dendrobium, including grey relation(GRA) and partial least squares(PLS) analysis were performed to evaluate the contribution of each identified component. The target peaks were identified by standards toge-ther with databases of Dendrobium, Nature Chemistry, MassBank, etc. Finally, four active components, including 3,5,9-trihydroxy-23-methylergosta-7,22-dien-6-one, diacylglycerol(14∶1/22∶6/0∶0), pipercitine, and 22-tricosenoic acid, might have negative effect on the growth of gastric cancer cells.
Subject(s)
Dendrobium , Humans , Least-Squares Analysis , Multivariate Analysis , Stomach Neoplasms/drug therapyABSTRACT
Spatial distribution uniformity is the critical quality attribute(CQA) of Ginkgo Leaves Tablets, a variety of big brand traditional Chinese medicine. The evaluation of the spatial distribution uniformity of active pharmaceutical ingredients(APIs) in Ginkgo Leaves Tablets is important in ensuring their stable and controllable quality. In this study, hyperspectral imaging technology was used to construct the spatial distribution map of API concentration based on three prediction models, further to realize the visualization research on the spatial distribution uniformity of Ginkgo Leaves Tablets. The region of interest(ROI) was selected from each Ginkgo Leaves Tablet, with length and width of 50 pixels, and a total of 2 500 pixels. Each pixel had 288 spectral channels, and the number of content prediction data could reach 1×10~5 for a single sample. The results of the three models showed that the Partial Least Squares(PLS) model had the highest prediction accuracy, with calibration set determination coefficient R_(pre)~2 of 0.987, prediction set determination coefficient R_(pre)~2 of 0.942, root mean square error of calibration(RMSEC) of 0.160%, and root mean square error of prediction(RMSEP) of 0.588%. The classical least-squares(CLS) model had a greater prediction error, with the RMSEP of 0.867%. Multivariate Curve Resolution-Alternating Least Square(MCR-ALS) model showed the worst predictive ability among the three models, and it couldn't realize content prediction. Based on the prediction results of PLS and CLS models, the spatial distribution map of APIs concentration was obtained through three-dimensional data reconstruction. Furthermore, histogram method was used to evaluate the spatial distribution uniformity of API. The data showed that the spatial distribution of APIs in Ginkgo Leaves Tablets was relatively uniform. The study explored the feasibility of visualization of spatial distribution of Ginkgo Leaves Tablets based on three models. The results showed that PLS model had the highest prediction accuracy, and MCR-ALS model had the lowest prediction accuracy. The research results could provide a new strategy for the visualization method of quality control of Ginkgo Leaves Tablets.
Subject(s)
Calibration , Ginkgo biloba , Least-Squares Analysis , Medicine, Chinese Traditional , Plant Leaves , Quality Control , Spectroscopy, Near-Infrared , TabletsABSTRACT
For the field detection problems of critical quality attribute(CQA) of moisture content in traditional Chinese medicine(TCM) manufacturing process, big brand TCM Tongren Niuhuang Qingxin Pills were used as the carrier, to establish a moisture content NIR field detection model with or without cellophane in real world production with use of near infrared(NIR) spectroscopy combined with stoichiometry. With the moisture content determined by drying method as reference value, the partial least square method(PLS) was used to analyze the correlation between the spectrum and the moisture reference value. Then the spectral pretreatment methods were screened and optimized to further improve the accuracy and stability of the model. The results showed that the best quantitative model was developed by the spectral data pretreatment of standard normal variate(SNV) with the latent variable factor number of 2 and 7 of Tongren Niuhuang Qingxin Pills with or without cellophane samples. The prediction coefficient of determination(R_(pre)~2) and standard deviation of prediction(RMSEP) of the model with cellophane samples were 0.765 7 and 0.157 2%; R_(pre)~2 and RMSEP of the model without cellophane samples were 0.772 2 and 0.207 8%. The NIR quantitative models of moisture content of Tongren Niuhuang Qingxin Pills with and without cellophane both showed good predictive performance to realize the rapid, accurate and non-destructive quantitative analysis of moisture content in such pills, and provide a method for the field quality control of the critical chemical attributes of moisture in the manufacturing of big brand TCM.
Subject(s)
Drugs, Chinese Herbal , Least-Squares Analysis , Medicine, Chinese Traditional , Spectroscopy, Near-InfraredABSTRACT
This study explores the emulsifying material basis of Angelicae Sinensis Radix volatile oil (ASRVO) based on partial least squares (PLS) method and hydrophile-lipophile balance (HLB) value.The turbidity of ASRVO emulsion samples from Gansu,Yunnan,and Qinghai was determined and the chemical components in the emulsion were analyzed by GC-MS.The PLS model was established with the chemical components as the independent variable and the turbidity as the dependent variable and evaluated with indexes R~2X and R~2Y.The chemical components which were in positive correlation with the turbidity were selected and the HLB values were calculated to determine the emulsification material basis of ASRVO.The PLS models for the 81 emulsion samples had high R~2X and R~2Y values,which showed good fitting ability.Seven chemical components,2-methoxy-4-vinylphenol,trans-ligustilide,3-butylidene-1(3H)-isobenzofuranone,dodecane,1-methyl-4-(1-methylethylidene)-cyclohexene,trans-beta-ocimene,and decane,had positive correlation with turbidity.Particularly,the HLB value of 2-methoxy-4-vinylphenol was 4.4,which was the HLB range of surfactants to be emulsifiers and 2-methoxy-4-vinylphenol was positively correlated with turbidity of the ASRVO emulsion samples from the main producing area.Therefore,2-methoxy-4-vinylphenol was the emulsifying material basis of ASRVO.The selected emulsifying substances can lay a foundation for exploring the emulsification mechanism and demulsification solution of ASRVO.
Subject(s)
China , Emulsions , Least-Squares Analysis , Oils, Volatile , Surface-Active AgentsABSTRACT
Near-infrared spectroscopy(NIRS) combined with band screening method and modeling algorithm can be used to achieve the rapid and non-destructive detection of the traditional Chinese medicine(TCM) production process. This paper focused on the ginkgo leaf macroporous resin purification process, which is the key technology of Yinshen Tongluo Capsules, in order to achieve the rapid determination of quercetin, kaempferol and isorhamnetin in effluent. The abnormal spectrum was eliminated by Mahalanobis distance algorithm, and the data set was divided by the sample set partitioning method based on joint X-Y distances(SPXY). The key information bands were selected by synergy interval partial least squares(siPLS); based on that, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA) and Monte Carlo uninformative variable(MC-UVE) were used to select wavelengths to obtain less but more critical variable data. With selected key variables as input, the quantitative analysis model was established by genetic algorithm joint extreme learning machine(GA-ELM) algorithm. The performance of the model was compared with that of partial least squares regression(PLSR). The results showed that the combination with siPLS-CARS-GA-ELM could achieve the optimal model performance with the minimum number of variables. The calibration set correlation coefficient R_c and the validation set correlation coefficient R_p of quercetin, kaempferol and isorhamnetin were all above 0.98. The root mean square error of calibration(RMSEC), the root mean square error of prediction(RMSEP) and the relative standard errors of prediction(RSEP) were 0.030 0, 0.029 2 and 8.88%, 0.041 4, 0.034 8 and 8.46%, 0.029 3, 0.027 1 and 10.10%, respectively. Compared with the PLSR me-thod, the performance of the GA-ELM model was greatly improved, which proved that NIRS combined with GA-ELM method has a great potential for rapid determination of effective components of TCM.
Subject(s)
Algorithms , Ginkgo biloba , Least-Squares Analysis , Plant Leaves , Spectroscopy, Near-InfraredABSTRACT
RESUMEN Objetivo: Analizar las propiedades psicométricas, estructura interna y relación con indicadores antropométricos del Body Shape Questionnaire (BSQ) en universitarios mexicanos, partiendo de un enfoque de la invarianza de medición. Métodos: Se realizó un estudio instrumental, orientado a la evaluación de las propiedades psicométricas, validez y fiabilidad, del BSQ. Se realizó análisis de invarianza de la medición por el método de estimación mínimos cuadrados ponderados con varianza ajustada y correlaciones policóricas, previa evaluación de diferentes modelos de medición del BSQ en cada grupo. Las puntuaciones de la versión final se correlacionaron con indicadores antropométricos mediante el coeficiente de correlación de Pearson. Resultados: En el análisis dimensional, todos los modelos previos del BSQ presentan índices de ajuste favorables, aunque aquellos de un solo factor presente son los que tienen evidencia más robusta. Se aceptó la invarianza configural, lo que indica que la estructura unidimensional es común a varones y mujeres. Sin embargo, las cargas factoriales de 16 ítems fueron estadísticamente diferentes entre los grupos, por lo que se descartaron y se obtuvo una versión de 18 ítems (BSQ-18), que se considera invariante respecto al sexo. Además, hay relación directa entre las puntuaciones de la versión del BSQ-18 y el índice de masa corporal, la circunferencia de cintura y el porcentaje de grasa. En cuanto a la fiabilidad, se hallaron indicadores satisfactorios. Conclusiones: El BSQ-18 es aplicable tanto a varones como a mujeres y tiene indicadores de fiabilidad elevados que posibilitan su uso en entornos clínicos para la evaluación en el abordaje de trastornos de la conducta alimentaria y obesidad en jóvenes universitarios.
ABSTRACT Objective: To analyse the psychometric properties, internal structure, and relationship with anthropometric indicators of the Body Shape Questionnaire (BSQ) among Mexican university students according to the measurement invariance approach. Methods: An instrumental study was carried out to assess the psychometric properties, validity, and reliability of the BSQ. The analysis of the measurement invariance was performed using the Least Squares Estimation, and weighted by adjusted variance and polychoric correlations after assessing different measurement models for BSQ in each group. The scores of the final version were correlated with anthropometric indicators by the Pearson correlation coefficient. Results: As regards the dimensional analysis, all of the previous models for BSQ have favourable adjustment rates, although those with a single factor show more robust evidence. The configural invariance was accepted; suggesting that the one-dimensional structure is common for both men and women. However, 16-item factorial loadings were statistically different between the groups. Hence, they were discarded and an 18-item version (BSQ-18) was obtained, which is considered invariant as regards gender. In addition, there is a direct relationship between the scores of the BSQ-18 version and the body mass index, waist circumference, and fat percentage. Satisfactory indicators were found as regards stability. Conclusions: The BSQ-18 can be used with men and women, and has high reliability indicators to be conducted in clinical settings to assess eating disorders and obesity among university students
Subject(s)
Humans , Male , Female , Adolescent , Somatotypes , Students , Feeding and Eating Disorders , Body Mass Index , Least-Squares Analysis , Surveys and Questionnaires , Waist Circumference , Gender IdentityABSTRACT
Introducción: Cuba ha sido afectada por la COVID-19. Todas las provincias del país han presentado casos confirmados de la enfermedad. Se han llevado a cabo medidas por parte del gobierno y el sistema de salud, para contrarrestar el contagio de persona a persona. Es de gran ayuda contar con estimaciones de casos confirmados para las decisiones. Objetivos: Obtener predicciones para los picos de casos confirmados y cantidad total de estos para algunas provincias de Cuba y para todo el país. Material y Métodos: Estudio de tipo predictivo de curvas de crecimiento poblacional. Se analizan los datos correspondientes a los primeros 52 días de afectación de la enfermedad en el país para estimar los modelos y aplicar el método de los mínimos cuadrados para modelos no lineales con respecto a los parámetros. Se utilizan el coeficiente de determinación ajustado, el criterio de información de Akaike y el error estándar de los residuos para medir la bondad del ajuste de los modelos. Se estudian las provincias del país que presentan una tasa de infectados por cien mil habitantes mayor que 14,71 y el país en su conjunto. Resultados: La bondad de ajuste de los modelos utilizados en las localidades estudiadas y en el país es alta, lo cual permite su confiabilidad para los pronósticos efectuados. Conclusiones: Las predicciones plantean que las cinco localidades analizadas presentan su pico de contagio en abril al igual que Cuba (AU)
Introduction: Cuba and all its provinces have been affected by COVID-19 disease. The government and the health system have taken measures to avoid contagion from person to person. To take these measures it is important to have estimates of the rate of infection. Objectives: To obtain predictions for the peak of infected cases and the total number for some Cuban provinces and the whole country. Material and Methods: Predictive study of population growth curves. Data from the first 52 days of the disease in the country are processed to estimate the models and to apply the method of least squares estimation of nonlinear parameters. The adjusted coefficient of determination, the Akaike information criterion and the standard error of the residuals are used to measure the goodness of fit of the models. The provinces that present a rate of infection per 100,000 inhabitants greater than 14,71 and the country as a whole are studied. Results: The goodness of fit of the models used in the provinces studied and the country is high, which allows them to be reliable for predictions. Conclusions: The predictions suggest that the five provinces analyzed and Cuba show their peak of contagion in April (AU)
Subject(s)
Humans , Waste Products , Population Growth , Least-Squares Analysis , Cuba , Growth ChartsABSTRACT
A rapid analysis method based on ultraviolet-visual(UV-Vis) spectroscopy, near infrared(NIR) spectroscopy and multivariable data analysis was established for quality evaluation of Shengxuebao Mixture. The contents of eight active ingredients of Shengxuebao Mixture including albiflorin, paeoniflorin, 2, 3, 5, 4'-tetra-hydroxy-stilbene-2-O-β-D-glucopyranoside, specnuezhenide,ecliptasaponin D, emodin, calycosin-7-glucoside and astragaloside Ⅳ were simultaneously detected by using this method. HPLC-UV-MS was used as a reference method for determining the contents of these ingredients. Partial least squares(PLS) analysis was implemented as a linear method for multivariate models calibrated between UV spectrum/NIR spectrum and contents of 8 ingredients. Finally, the performance of the model was evaluated by 24 batches of test samples. The results showed that both UV-Vis and NIR models gave a good calibration ability with an R~2 value above 0.9, and the prediction ability was also satisfactory, with an R~2 value higher than 0.83 for UV-Vis model and higher than 0.79 for NIR model. The overall results demonstrate that the established method is accurate, robust and fast, therefore, it can be used for rapid quality evaluation of Shengxuebao Mixture.
Subject(s)
Calibration , Chromatography, High Pressure Liquid , Drugs, Chinese Herbal , Least-Squares Analysis , Mass Spectrometry , Spectroscopy, Near-InfraredABSTRACT
Subject(s)
Animals , Cause of Death , Diagnosis , Dogs , Erythrocyte Count , Fourier Analysis , Incidence , Least-Squares Analysis , Leukocyte Count , Mass Screening , Methods , Principal Component AnalysisABSTRACT
OBJECTIVES: The association between the spread of infectious diseases and climate parameters has been widely studied in recent decades. In this paper, we formulate, exploit, and compare three variations of the susceptible-infected-recovered (SIR) model incorporating climate data. The SIR model is a well-studied model to investigate the dynamics of influenza viruses; however, the improved versions of the classic model have been developed by introducing external factors into the model. METHODS: The modification models are derived by multiplying a linear combination of three complementary factors, namely, temperature (T), precipitation (P), and humidity (H) by the transmission rate. The performance of these proposed models is evaluated against the standard model for two outbreak seasons. RESULTS: The values of the root-mean-square error (RMSE) and the Akaike information criterion (AIC) improved as they declined from 8.76 to 7.05 and from 98.12 to 93.01 for season 2013/14, respectively. Similarly, for season 2014/15, the RMSE and AIC decreased from 8.10 to 6.45 and from 117.73 to 107.91, respectively. The estimated values of R(t) in the framework of the standard and modified SIR models are also compared. CONCLUSIONS: Through simulations, we determined that among the studied environmental factors, precipitation showed the strongest correlation with the transmission dynamics of influenza. Moreover, the SIR+P+T model is the most efficient for simulating the behavioral dynamics of influenza in the area of interest.
Subject(s)
Basic Reproduction Number , Climate , Communicable Diseases , Epidemiology , Humidity , Influenza, Human , Iran , Least-Squares Analysis , Orthomyxoviridae , SeasonsABSTRACT
PURPOSE: The aim of this study was to evaluate the influence of polishing methods on the color stability of composite resins. MATERIALS AND METHODS: Two bulk-fill and four conventional resin composites were filled in cylindrical molds (6 mm diameter, 4 mm height) and light-cured. The specimens were stored in 34℃ distilled water for 24 h. Spectrophotometer was used to determine the color value according to the CIE L(*)a(*)b(*) color space. Each group was divided into three groups according to polishing methods (n = 5). Group 1 was control group (Mylar strip group), group 2 was polished with PoGo, and group 3 was polished with Sof-Lex Spiral wheels. Color evaluation was performed weekly for 4 weeks after immersion in 34℃ distilled water. The results were analyzed by generalized least squares method (P < 0.05). RESULTS: Generalized least squares analysis revealed that Sof-Lex Spiral wheels group showed the significantly lower ΔE values compared to PoGo and control group (P < 0.05). The ΔE values of polished group showed the significantly lower than the ΔE values of unpolished group (P < 0.05). Regarding color changes of composite resins, there was no significant difference between the ΔE values of Filtek Z250 and Filtek Z350 XT Universal restorative in all time intervals (P < 0.05). Tetric N-Ceram Bulk Fill showed the significantly lower ΔE values compared to other composite resins in 1, 2, 3 weeks (P < 0.05). CONCLUSION: Within the limitations of this study, polishing methods influence the color stabilities of composite resins. The group polished with Sof-Lex Spiral Wheels showed more resistance to discoloration than group polished with PoGo.
Subject(s)
Absorption , Composite Resins , Fungi , Immersion , Least-Squares Analysis , Methods , WaterABSTRACT
OBJECTIVE@#To investigate the effects of Pinggan Prescription (, PGP) on hypertension by the associated methods of metabonomic and pharmacodynamic.@*METHODS@#A total of 32 male spontaneously hypertensive rats (SHRs) were randomly divided into two groups by using the random number table method: a treatment group (n=18) and a model group (n=14). The Wistar rats (n=14) were used as the normal group. Different prescription were used to intervene three groups: the treatment group in which PGP extract was administered orally at a dose of 18.336 g/kg (PGP/body weight), and the model group in which physiological saline was administered at the equivalent dose. The same treatment was applied to the normal group as the model group. The blood pressure was measured by tail-cuff method, and pharmacodynamic indexes including cyclic adenosine monophosphate (cAMP) and angiotensin II (Ang II) were tested by enzyme-linked immunosorbent assay. The plasma samples from three groups were detected by gas chromatography-mass spectrometry (GC-MS).@*RESULTS@#Compared with the model group, blood pressure of treatment group was obviously reduced after continuous curing with PGP (P<0.01). The pharmacodynamic results illustrated that the content of Ang II increased with the raised blood pressure and the cAMP expressed the converse trend. After curing with PGP, the content of Ang II decreased, the difference between model group and treatment group was significant (P<0.01), and the cAMP expressed the converse trend. Five potential biomarkers were identified, including arachidonic acid, hexadecanoic acid, elaidic acid, octadecanedioic acid and 9,12-octadecadienoic acid. These metabolites had shown significantly changes as followed: arachidonic acid, hexadecanoic acid and elaidic acid were significantly higher and octadecanedioic acid and 9,12-octadecadienoic acid were lowered in the model group than those in the normal group. After the treatment of PGP, the metabolites had the trends of returning to normal along with the reduced blood pressure.@*CONCLUSIONS@#PGP intervention for hypertension played a major role in the metabolism of arachidonic acid and linoleic acid. Metabonomic with pharmacodynamic methods could be potentially powerful tools to investigate the mechanism of Chinese medicine.
Subject(s)
Animals , Biomarkers , Blood , Discriminant Analysis , Drugs, Chinese Herbal , Pharmacology , Gas Chromatography-Mass Spectrometry , Hypertension , Blood , Drug Therapy , Least-Squares Analysis , Male , Metabolic Networks and Pathways , Metabolomics , Models, Biological , Principal Component Analysis , Rats, Inbred SHR , Rats, WistarABSTRACT
The quantity of biomass, glucose concentration and ethanol concentration are important parameters in ethanol fermentation. Traditional methods are usually based on samples for off-line measurement, which not only requires multiple instruments for test and analysis but also consumes notable time and effort, and therefore is inconvenient for real-time process control and optimization. In this study, an in-situ detection method based on the near-infrared (NIR) spectroscopy is proposed for measuring the above process parameters in real time. The in-situ measurement is carried out by using an immersion type NIR spectroscopy. A multi-output prediction model for simultaneously estimating the quantity of glucose, biomass and ethanol is established based on a multi-output least-squares support vector regression algorithm. The experimental results show that the proposed method can precisely measure the quantity of glucose, biomass and ethanol during the ethanol fermentation process. Compared to the existing partial-least-squares method for modeling and prediction of individual components, the proposed method could evidently improve the measurement accuracy and reliability.